![]() ![]() But when it comes to flexibility, Matplotlib beats Seaborn. It is simple to use and can be used to directly generate graphs out of a dataframe. It has great capabilities for statistical visualizations. Seaborn is also a visualization library similar to Matplotlib built on top of Matplotlib. Changing Figure Size Of A Contou Seaborn vs. The most basic way is setting the figsize parameter in the plt.figure() function. You can change the figure size of a contour plot just like a 2d plot. How to change the figure size of a contour plot? Related: Learn how we can plot different contour plots using Matplotlib. Then we set the colour bar based on our contour. Then we set the names for axes and the title. Then we plotted a contour using the values of X, Y, and Z and set the color mapping as viridis. Then we generated a figure using the plt.figure() function. After that, we generated the values of z as the sine of the square root of the sum of x squared and y squared. Then we created a grid of these numbers using the meshgrid method of numpy and stored the matrices in X and Y. Then we created a series of 100 numbers from -5 to 5 using the linspace method of numpy. To do this, we just have to add the following code line before the plt.scatter line.Īx = fig.add_subplot(111, projection='3d')Ĭontour_plot = ax.plot_surface(X, Y, Z, cmap='viridis')Īx.set_title('Three-Dimensional Contour Plot')įirst, we imported the necessary packages. Let’s try to set the figure size to 4×3 inches. We can manipulate the figure according to our convenience using this figsize parameter. ![]() The first float is the height, and the second one is the width of the figure which will be displayed. The figsize parameter takes a tuple of two floats as the argument. This figure() method has a parameter figsize. The matplotlib.pylot module has a figure() method which regulates the features of the figure which will be displayed. When you plot a graph, the default parameters for the figure size the 6.4×4.8 inches. Changing figure size using figsize argument There are a number of ways in which you can change the size of the figure. But sometimes, we may need to change the size of this figure. It fits perfectly on a standard computer screen. This size is standard for any graph produced by the matplotlib library. That means it has a height of 4.8 inches and a width of 6.4 inches. The size of this graph produced using the matplotlib.pyplot module has a default size of 6.4×4.8 inches. Well, we can clearly understand that Python was invented in 1991 seeing this, so yeah, we did it. We can make a beautiful and simple scatter plot. Scatter Plot Programming Language Foundation Year In the end, we used the plt.show() method to display the actual graph. Then we set the title and labels of the axes. We passed programming language as the X-axis and Foundation year as the Y-axis. ![]() After that, plot the scatter plot using () which takes 2 arguments – the X-axis and the Y-axis. First, we read the CSV file and store the data in a dataframe using the pandas library. ![]() To plot the graph, we’re going to use the pyplot module of the Matplotlib library. Plt.title("Programming language-Foundation year scatter plot") If you don’t know how to create a dataframe, make sure to check out this article where we learn to create a dataframe. We’re going to create the scatter plot based on a dataframe that we created in one of the previous articles. Let’s try to create a scatter plot using Matplotlib. It’s very common, well-known, and really powerful. Plotting a simple scatter plot using MatplotlibĪ scatter plot is a simplistic 2-dimensional graph. On our way, we’re going to check different ways in which you can change the size of figures drawn with matplotlib. In this article, we’re going to dive deep into the world of data science and explore the matplotlib library. There are many ways in which you can achieve this. An important feature is its ability to change the figure size. It allows you to control every aspect of the visual you create. The Matplotlib library provides a lot of flexibility in creating graphs and designs. Along with numPy, Pandas, SciPy, and many more, it lays down the basis of data science in the most easiest way possible. And at the heart of it stands Matplotlib, an amazing library for creating all kinds of visuals of the data. One of the key fields where Python shines is Data Science, with no other language there that stands out like Python. ![]()
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